Data & Annotation Team
Development Seed’s Data and Annotation Team produces high-quality geodata and machine learning labels.
High-quality data is crucial in Geospatial and Machine learning fields for making any project successful.
Why it matters
Our Data Team is one of the most prolific and accomplished mapping teams in the world. We have extensive experience with OpenStreetMap, the open geospatial software ecosystem, and its metadata standards. We are also at the forefront of building open and highly efficient workflows for mapping and building complex machine learning training datasets. To illustrate, our team edited more than 25 million objects in OpenStreetMap so far.
Data quality
Data quality is a fundamental aspect that improves the outcomes of our projects, so we combine advanced machine learning methods with highly-trained mappers to quickly and consistently produce pixel-perfect maps and imagery annotation.Vastly speed in mapping and annotation
We vastly speed up mapping through the use of machine learning. We have developed and refined detection algorithms for most common features including settlements and buildings, road and power infrastructure, and agriculture and land features. Aided by these algorithms, our Data and Annotation Team can work up to 30x faster.
Fast, high quality mapping and validation for the World Bank
We mapped the full power grid of Zambia, Nigeria, and Pakistan with the World Bank. Specifically, we added over 125k features (transformers, poles, switches, substations) and over 28k kilometers of power lines to OpenStreetMap.
High quality training data
We produced image classification, object detection and semantic segmentation training datasets for our ML projects.
Creating over 15,000 annotations for building construction quality, structure, and materials used for training ML model for categorizing buildings according to risk.
Labeling schools to apply scalable machine learning over high-resolution satellite imagery and validating the ML inferences to map every school in several countries from Africa, Asia and South America.
Efficient workflows for high speed mapping
We build and customize a variety of tools in order to maximize speed while maintaining extremely high accuracy in our data generation and validations workflows. Some of our main tools are Chips-ahoy and Relabeler, for ML output validations; Geokit, for data analysis and processing; and osm-seed tool that provides an easily installable package for the OpenStreetMap software stack. You can read more about our tools and workflows.
Types of mapping
| COMPONENT | STARTING AT* (USD) | |
|---|---|---|
| Building | ||
| Low density mapping | km² | $23.00 |
| Intermediate density mapping | km² | $138.00 |
| High density mapping | km² | $344.00 |
| Roads | ||
| Low density mapping | km² | $5.00 |
| Intermediate density mapping | km² | $9.00 |
| High density mapping | km² | $9.00 |
| Farm Field | ||
| Low Complexity | per farm | $0.58 |
| Medium Complexity | per farm | $0.88 |
| High Complexity | per farm | $1.75 |
(*)Base Price
Types annotation
| Annotation type | condition | average | starting at* (USD) |
|---|---|---|---|
| Classification | 1 class per image | 1,000 images/hour | $35.00 |
| Object detection | 3 classes per image | 80 images/hour | $35.00 |
| Image Segmentation | 6 classes per image | 4 images/hour | $35.00 |
(*)Base Price
Please reach out to [email protected] to discuss your project needs and pricing estimate.